Publikation: Better models by discarding data?
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In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC1/2, that can be used for this purpose were characterized and it was shown that CC1/2 has superior properties compared with "merging" R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC1/2 and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled "paired-refinement" tests show that even though discarding the weaker data leads to improvements in the merging R values, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the merging R values. Interestingly, in all of these tests CC1/2 is the one data-quality indicator for which the behaviour accurately reflects which of the alternative data-handling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.
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DIEDERICHS, Kay, P. Andrew KARPLUS, 2013. Better models by discarding data?. In: Acta Crystallographica Section D : Biological Crystallography. 2013, 69(7), pp. 1215-1222. ISSN 0907-4449. eISSN 1399-0047. Available under: doi: 10.1107/S0907444913001121BibTex
@article{Diederichs2013-07Bette-26309,
year={2013},
doi={10.1107/S0907444913001121},
title={Better models by discarding data?},
number={7},
volume={69},
issn={0907-4449},
journal={Acta Crystallographica Section D : Biological Crystallography},
pages={1215--1222},
author={Diederichs, Kay and Karplus, P. Andrew}
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